Podcast: How to operate smarter and leaner, thanks to Little’s Law

Anna Townshend is managing editor of Plant Services and has been a journalist and editor for almost 20 years. In addition to writing and editing thousands of articles in her career, she has been an active speaker on industry panels and presentations. In this episode of Great Question: A Manufacturing Podcast, Anna reads a piece that is part of our new Maintenance Mindset feature, where each week one of the Plant Services editors highlights important and interesting nuggets in the news about manufacturing and asset management.

Below is an excerpt from the podcast:

John D.C. Little, professor emeritus at the Massachusetts Institute of Technology Sloan School of Management, who had been part of MIT for nearly 80 years, died on September 27. If you’re familiar with him, you probably know his namesake theorem Little’s Law. He also innovated fields like operations research and marketing science, particularly by using computing methods to measure and make business decisions.

I want to focus on how his work in data and decision sciences applies to manufacturing. Little’s Law, proven in 1961, is an operations research concept for understanding the dynamics of queuing, and it’s widely used in manufacturing and many other industries too.

It’s a simple formula to describe the relationship between how many things are waiting in line, how often new ones arrive, and how long each thing takes to reach completion. Little’s Law is a fundamental concept of lean manufacturing, based on the relationship between inventory, throughput, and lead time. If a manufacturer can understand how these main factors interact, it will have a better basic understanding of its system. Just like the line at the drive-thru, some production lines move faster than others, and Little’s Law can mathematically tell you why.

Broadly, the theorem describes how the long-term average number of items (L) in a stationary system is equal to the long-term throughput (λ) multiplied by the average time (W) that an item spends waiting in the system. It applies to many sectors, from manufacturing to health care to customer service.